2 citations · 3 across the 6 of their papers we have counts for
7 papers
Multimodal Federated Learning under Dual-Axis Modality Missingness
Adiba Orzikulova, Jaehyun Kwak, Jaemin Shin +5
Multimodal federated learning (FL) supports collaborative modeling in privacy-sensitive health-sensing and medical settings, but realistic deployments often exhibit dual-axis modal…
VibOmni: Towards Scalable Bone-conduction Speech Enhancement on Earables
Lixing He, Yunqi Guo, Haozheng Hou +1
Earables, such as True Wireless Stereo earphones and VR/AR headsets, are increasingly popular, yet their compact design poses challenges for robust voice-related applications like…
ProAgent: Harnessing On-Demand Sensory Contexts for Proactive LLM Agent Systems in the Wild
Bufang Yang, Lilin Xu, Liekang Zeng +8
Recent studies have begun to explore proactive large language model (LLM) agents that provide unobtrusive assistance by automatically leveraging contextual information, such as in…
CoHear: Conversation Enhancement via Multi-Earphone Collaboration
Lixing He, Yunqi Guo, Zhenyu Yan +1
In crowded places such as conferences, background noise, overlapping voices, and lively interactions make it difficult to have clear conversations. This situation often worsens the…
OpenTCM: A GraphRAG-Empowered LLM-based System for Traditional Chinese Medicine Knowledge Retrieval and Diagnosis
Jinglin He, Yunqi Guo, Lai Kwan Lam +6
Traditional Chinese Medicine (TCM) represents a rich repository of ancient medical knowledge that continues to play an important role in modern healthcare. Due to the complexity an…
SocialMind: LLM-based Proactive AR Social Assistive System with Human-like Perception for In-situ Live Interactions
Bufang Yang, Yunqi Guo, Lilin Xu +4
Social interactions are fundamental to human life. The recent emergence of large language models (LLMs)-based virtual assistants has demonstrated their potential to revolutionize h…
Visualizing the Invisible: A Generative AR System for Intuitive Multi-Modal Sensor Data Presentation
Yunqi Guo, Kaiyuan Hou, Heming Fu +4
Understanding sensor data can be difficult for non-experts because of the complexity and different semantic meanings of sensor modalities. This leads to a need for intuitive and ef…